Deep learning methods for early detection of Alzheimer's disease using structural MR images: a survey

In this paper, we present an extensive review of the most recent works on Alzheimer's disease (AD) prediction, focusing on Moderate Cognitive Impairment (MCI) conversion prediction. We aimed to identify the most useful brain-magnetic resonance imaging (MRI) biomarkers and deep learning frameworks used for prediction. To achieve this, we analyzed more than 130 studies and reviewed 7 articles. A closer examination revealed that the hippocampus is an important region of interest (ROI) affected early by AD, and many related features help detect the disease in its early stages. However, when considered alone, this ROI is not sufficient to ensure high prediction performance. Therefore, several other brain regions can also provide additional information to improve prediction accuracy. Concerning state-of-the-art deep neural networks, the U-Net represents the most efficient architecture for hippocampus segmentation. The RESU-Net architecture achieved the highest Dice Similarity Coefficient (DSC) value, equal to 94%.For MCI conversion prediction, the best results were obtained by two models identifying significant landmarks from the entire brain for classification. The multi-stream convolutional neural network achieved the best Area Under the Curve (AUC) and specificity of 94.39% and 99.70%, respectively. Finally, a region ensemble model delivered the highest accuracy of 85.90%, highlighting the need for further research to address this challenging problem.